Our approach of assigning a random binary vector to each user is related to distributed output representation [1]}, where a binary function is learned for each bit position. It follows [2]} in that functions are chosen to be meaningful and independent, so that each combination of concepts can be represented by a unique representation. Another related method is distributed output coding [3]}, [4]}, which uses error-correcting codes (ECCs) to improve the generalization performance of classifiers, with the codes constructed such that the length of codewords is greater or equal to number of classes. We, however, use random binary vectors as user embeddings to enable privacy-preserving training of UA models in the federated setting. Moreover, we propose to generate vectors of length much smaller than the number of users to improve the scalability of the method to large number of users.
